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Efficiency of Using Wavelet Transforms for Filtering Noise in the Signals of Measuring Transducers

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Measurement Techniques Aims and scope

The article discusses methods of wavelet filtering of noise in signals of measuring transducers using the threshold method of discrete wavelet transform. Special model signals were used to study the methods of wavelet filtering of noise, which enabled estimation of the filtering errors. A method has been developed for determining the parameters with a threshold for all levels of decomposition, which can be used to determine the wavelet function, threshold function, and filtering threshold of the detailing coefficients of the discrete wavelet decomposition. The influence of the parameters of the noise distribution, noise level, number of vanishing moments of the Daubechies wavelet function, nature of the threshold function, and threshold value on the filtering error caused by the noises of non-stationary measuring signals have been investigated by computational experiment. The article presents the results of the study of six threshold functions with the addition of noise to the measuring signal with non-stationary amplitude, frequency, and duty cycle of square-wave pulses. The signal of the Doppler sensors was analyzed and the wavelet filtering parameters were calculated, with the minimum error. The parameters were used to construct graphs of signals before and after filtering directly in the time domain using the inverse wavelet transform.

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References

  1. K. G. Polovenko, “Large-scale analysis of electroencephalograms based on wavelet transforms with the basis Daubechies function,” Prikl. Radioelektron., 10, No. 1, 15–21 (2011).

    Google Scholar 

  2. C. I. Salis, A. E. M. Paschalis, A. Bizopoulos, et al., 13th IEEE Int. Conf. on Bioinformatics and Bioengineering, Chania, Greece, Nov. 10–13, 2013, IEEE (2013), pp. 1–4, https://doi.org/10.1109/BIBE.2013.6701613.

  3. A. S. Kyzdarbekova, K. B. Kasymbekova, D. M. Dumbayeva, and U. S. Kyzdarbek, “Algorithm for extracting features and removing noise of an electrocardiosignal based on wavelet transform,” Probl. Sovr. Nauki Obraz., No. 7(89), 112–116 (2017).

  4. F. Ehrentreich, Anal. Bioanal. Chem., 372, No. 1, 115–121 (2002), https://doi.org/10.1007/s00216-001-1119-4.

    Article  Google Scholar 

  5. X.-Q. Lu, H.-D. Liu, and J.-W. Kang, Anal. Chim. Acta, 484, No. 2, 201–210 (2003), https://doi.org/10.1016/S0003-2670(03)00309-X.

    Article  Google Scholar 

  6. B. K. Alsberg, A. M. Woodward, M. K. Winson, et al., Analyst, 122, No. 7, 645–652 (1997), https://doi.org/10.1039/A608255F.

    Article  ADS  Google Scholar 

  7. B. Zhang, L.-X. Sun, H.-B. Yu, et al., J. Anal. At. Spectrom., 28, No. 12, 1884–1893 (2013), https://doi.org/10.1039/c3ja50239b.

    Article  ADS  Google Scholar 

  8. I. Mappe-Fogaing, L. Joly, G. Durry, et al., Appl. Spectrosc., 66, No. 6, 700–710 (2012), https://doi.org/10.1366/11-06459.

  9. M. S. Niu and G.-S. Wang, Wuli Xuebao, 66, No. 2, 024202 (2017).

    Google Scholar 

  10. H. Xia, F. Dong, Z. Zhang, et al., Adv. Sens. Sys. Appl. IV, 7853, 785311 (2010), https://doi.org/10.1117/12.871629.

    Article  Google Scholar 

  11. J. Li, U. Parchatka and H. Fischer, Appl. Phys. B, 108, No. 4, 951–963 (2012), https://doi.org/10.1007/s00340-012-5158-7.

    Article  ADS  Google Scholar 

  12. C. T. Zheng, W.-L. Ye, J.-Q. Huanga, et al., Sens. Actuat. B, Chem., 190, 249–258 (2014), https://doi.org/10.1016/j.snb.2013.08.055.

  13. P. M. Ramos and I. Ruisanchez, J. Raman Spectrosc., 36, No. 9, 848–856 (2005), https://doi.org/10.1002/jrs.1370.

    Article  ADS  Google Scholar 

  14. I. R. Edu, F. C. Adochiei, T. L. Grigorie, and R.M. Botez, 3rd Int. Workshop on Numerical Modeling in Aerospace Sciences, Bucharest, Romania, May 6–7, 2015, INCAS Bull., 7, No. 2, 71–80 (2015), https://doi.org/10.13111/2066-8201.2015.7.2.7.

  15. Yu. E. Voskoboinikov and D. A. Krysov, “Wavelet filtering of noises of various statistical nature,” Sovr. Naukoemk. Tekhnol., No. 6, 50–54 (2018).

  16. O. Oliynyk, Y. Taranenko, D. Losikhin, and A. Shvachka, East.-Europ. J. Enterp. Technol., 4, No. 4 (94), 36–42 (2018), https://doi.org/10.15587/1729-4061.2018.140649.

  17. T. V. Bykova and G. A. Cherepashchuk, “Uncertainty of the wavelet recovery of the dynamic measurements results,” Sist. Obrob. Inform. Khar’kov, 6, No. 64, 10–12 (2007).

    Google Scholar 

  18. Bauman National Library, https://ru.bmstu.wiki/Вейвлет-преобразование#, acc. Jul. 6, 2020.

  19. B. S. Gurevich, S. B. Gurevich, and V. V. Manoilov, “Wavelet filtering of spatial frequencies at discretization of light fields,” Nauchn. Priborostr., 22, No. 1, 101–106 (2012).

    Google Scholar 

  20. Yu. E. Voskoboinikov, “Wavelet fi ltering with two-parameter threshold functions: function selection and estimation of optimal parameters,” Avtomat. Progr. Inzh., No. 1 (15), 69–78 (2016).

  21. G. R. Lee, R. Gommers, F. Wasilewski, et al., J. Open Source Softw., 4 (36), 1237 (2019), https://doi.org/10.21105/joss.01237.

  22. A. S. Yasin, Filtration of Noise-Contaminated Signals and Images Using Wavelet Transform: PhD Thesis in Phys. Math. Sci., Saratov State University, Saratov (2016).

  23. E. V. Burnaev and N. N. Olenev, “Proximity measure for time series based on wavelet coeffi cients,” in: Proc. 48th Sci. Conf. of the Moscow Institute of Physics and Technology, Moscow–Dolgoprudny (2005), Pt. VII, pp. 108–110.

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  1. Private Enterprise “Likopak”, Dnipro, Ukraine

    Yu. K. Taranenko

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  1. Yu. K. Taranenko
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Correspondence to Yu. K. Taranenko.

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Translated from Izmeritel’naya Tekhnika, No. 2, pp. 16–21, February, 2021.

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Taranenko, Y.K. Efficiency of Using Wavelet Transforms for Filtering Noise in the Signals of Measuring Transducers. Meas Tech 64, 94–99 (2021). https://doi.org/10.1007/s11018-021-01902-8

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